RALF22 is a key modulator of the root hair growth responses to fungal ethylene emissions in Arabidopsis (Rafael Jorge León Morcillo, Jesús Leal-López, Alberto Férez-Gómez, et. al.) buff.ly/3B5B8SV Rafa Morcillo Biostimulators IHSM IHSM La Mayora ASPB #PlantSci

UPDATE: The transcriptional integration of environmental cues with root cell type development (Mona Gouran, Siobhan M Brady) buff.ly/3XvoROX ASPB #PlantSci

Optimizing vertical leaf area and nitrogen distribution in maize canopies maximizes radiation use efficiency (RUE), enhancing crop productivity—key for high-throughput phenotyping and screening. #CropScience Details: spj.science.org/doi/10.34133/p…

New multimodal PDC-VLD model detects tomato leaf diseases using image-text pairs, reducing manual annotation costs and improving accuracy in data-scarce environments. #Agritech #AIart Details: spj.science.org/doi/10.34133/p…

Precision Plots Analyzer (PREPs) offers easy, GIS-free plant phenotyping from SfM-MVS images on 64-bit Windows. It estimates crop traits like height and coverage. #AgTech Details: spj.science.org/doi/10.34133/p…

MTSC-Net boosts slash pine shoot counting accuracy with semi-supervised learning, combining VGG19 and attention fusion for efficient feature extraction. Great for tree breeding! Details: spj.science.org/doi/10.34133/p…

Quantitative 3D leaf shape analysis in 478 corn inbred lines reveals genetic and environmental impacts, advancing insights into corn leaf morphology and its complex regulation. #Agriculture #Genetics #plantscience Details: spj.science.org/doi/10.34133/p…

We evaluate NeRFs for 3D plant reconstruction in various environments, achieving 74.6% F1 score and reducing training time by half with early stopping. #AI #plantscience Details: spj.science.org/doi/10.34133/p…

We improved sorghum panicle detection and grain number estimation using AI from smartphone images, achieving a 17% error. Boosting crop yield via tech! #AI #Agriculture Details: spj.science.org/doi/10.34133/p…

We developed CSNet, a count-supervised network for accurate wheat ear counting, outperforming position-supervised methods and reducing labor costs. Code: csnet.samlab.cn #AI #Agriculture #FoodSecurity Details: spj.science.org/doi/10.34133/p…

We compared leaf spectral reflectance and chlorophyll fluorescence for drought response in tobacco and barley. Chlorophyll fluorescence proved more reliable, but both methods are recommended. #ClimateChange #PlantScience #DroughtResearch Details: spj.science.org/doi/10.34133/p…

We developed CNN models using UAV data to estimate biomass-related traits in soybean, improving breeding efficiency. Latent features showed strong potential for genomic predictions. #AI #Agriculture #breedingbottom Details: spj.science.org/doi/10.34133/p…

We developed Auto-LIA, a noninvasive, automated system for leaf inclination angle measurement, achieving 2.5° accuracy compared to LiDAR, at a lower cost. Code: autolia.samlab.cn #AI #Agriculture #plantscience Details: spj.science.org/doi/10.34133/p…

We developed AFM-YOLOv8s for rapid and accurate sporangium detection, improving accuracy by 2.7% over YOLOv8 for downy mildew. Available in a web app. #AI #Agriculture #PlantHealth Details: spj.science.org/doi/10.34133/p…

We introduced the Fruit Water Stress Index (FWSI) using 3D thermal point clouds of apple trees to monitor water stress, offering precise, spatiotemporal insights for crop management. #Agriculture #AI #ClimateChange Details: spj.science.org/doi/10.34133/p…

Exploring RSA in alfalfa with AI: New study uses ResNet-18 to boost prediction accuracy by 13%. Combats label noise with CL/RL, enhancing plant trait analysis. #PlantScience Details: spj.science.org/doi/10.34133/p…

New study enhances rapeseed yield prediction with WOFOST model, accounting for silique photosynthesis, improving accuracy in southern Hunan. Details: spj.science.org/doi/10.34133/p…

MG-YOLO, an advanced detection algorithm, achieves 98.3% accuracy in gray mold spore detection, outperforming YOLOv5 models . Details: spj.science.org/doi/10.34133/p…

Hyperspectral tech advances Neopyropia quality assessment, revealing pigment profiles non-invasively and rapidly. #MacroalgaeInnovation Details: spj.science.org/doi/10.34133/p…

Developed an AI-powered system to monitor cotton Verticillium wilt with 93.2% accuracy, improving efficiency and precision for cotton breeding and disease resistance research. Details: spj.science.org/doi/10.34133/p…